Low-Complexity Neural Wind Noise Reduction for Audio Recordings

Fuente: arXiv
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Main Authors: Eftekhari, Hesam, Chetupalli, Srikanth Raj, Shetu, Shrishti Saha, Habets, Emanuël A. P., Thiergart, Oliver
Format: Preprint
Published: 2025
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author Eftekhari, Hesam
Chetupalli, Srikanth Raj
Shetu, Shrishti Saha
Habets, Emanuël A. P.
Thiergart, Oliver
author_facet Eftekhari, Hesam
Chetupalli, Srikanth Raj
Shetu, Shrishti Saha
Habets, Emanuël A. P.
Thiergart, Oliver
contents Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose a low-complexity single-channel deep neural network that leverages the spectral characteristics of wind noise. Experimental results show that our method achieves performance comparable to the state-of-the-art low-complexity ULCNet model. The proposed model, with only 249K parameters and roughly 73 MHz of computational power, is suitable for embedded and mobile audio applications.
format Preprint
id arxiv_https___arxiv_org_abs_2507_01821
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Low-Complexity Neural Wind Noise Reduction for Audio Recordings
Eftekhari, Hesam
Chetupalli, Srikanth Raj
Shetu, Shrishti Saha
Habets, Emanuël A. P.
Thiergart, Oliver
Audio and Speech Processing
Sound
Signal Processing
Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose a low-complexity single-channel deep neural network that leverages the spectral characteristics of wind noise. Experimental results show that our method achieves performance comparable to the state-of-the-art low-complexity ULCNet model. The proposed model, with only 249K parameters and roughly 73 MHz of computational power, is suitable for embedded and mobile audio applications.
title Low-Complexity Neural Wind Noise Reduction for Audio Recordings
topic Audio and Speech Processing
Sound
Signal Processing
url https://arxiv.org/abs/2507.01821